class_docstr: Computes cosine similarities between word embeddings and retrieves most
  similar terms for a given term.
  
  Notes
  -----
  By fitting the word embeddings to a vocabulary that you will be using, you
  can eliminate all out-of-vocabulary (OOV) words that you would otherwise
  receive from the `most_similar` method. In subword models such as fastText,
  this procedure will also infer word-vectors for words from your vocabulary
  that previously had no word-vector.
  
  >>> from gensim.test.utils import common_texts, datapath
  >>> from gensim.corpora import Dictionary
  >>> from gensim.models import FastText
  >>> from gensim.models.word2vec import LineSentence
  >>> from gensim.similarities import WordEmbeddingSimilarityIndex
  >>>
  >>> model = FastText(common_texts, vector_size=20, min_count=1)  # train word-vectors on a corpus
  >>> different_corpus = LineSentence(datapath('lee_background.cor'))
  >>> dictionary = Dictionary(different_corpus)  # construct a vocabulary on a different corpus
  >>> words = [word for word, count in dictionary.most_common()]
  >>> word_vectors = model.wv.vectors_for_all(words)  # remove OOV word-vectors and infer word-vectors for new words
  >>> assert len(dictionary) == len(word_vectors)  # all words from our vocabulary received their word-vectors
  >>> termsim_index = WordEmbeddingSimilarityIndex(word_vectors)
  
  Parameters
  ----------
  keyedvectors : :class:`~gensim.models.keyedvectors.KeyedVectors`
      The word embeddings.
  threshold : float, optional
      Only embeddings more similar than `threshold` are considered when retrieving word embeddings
      closest to a given word embedding.
  exponent : float, optional
      Take the word embedding similarities larger than `threshold` to the power of `exponent`.
  kwargs : dict or None
      A dict with keyword arguments that will be passed to the
      :meth:`~gensim.models.keyedvectors.KeyedVectors.most_similar` method
      when retrieving the word embeddings closest to a given word embedding.
  
  See Also
  --------
  :class:`~gensim.similarities.levenshtein.LevenshteinSimilarityIndex`
      Retrieve most similar terms for a given term using the Levenshtein distance.
  :class:`~gensim.similarities.termsim.SparseTermSimilarityMatrix`
      Build a term similarity matrix and compute the Soft Cosine Measure.
code: def most_similar(self, t1, topn=10):
      if t1 not in self.keyedvectors:
          logger.debug('an out-of-dictionary term "%s"', t1)
      else:
          most_similar = self.keyedvectors.most_similar(
              positive=[t1], topn=topn, **self.kwargs
          )
          for t2, similarity in most_similar:
              if similarity > self.threshold:
                  yield (t2, similarity**self.exponent)
  
imports: from array import array
  from itertools import chain
  import logging
  from math import sqrt
  import numpy
  from scipy import sparse
  from gensim.matutils import corpus2csc
  from gensim.utils import SaveLoad, is_corpus
label_desc: Use image node_id 2 for calling the WordEmbeddingSimilarityIndex obj's underlying member method code with example usage: obj.most_similar(t1, topn) without return types